4.7 Article

Privacy-Preserving Object Detection for Medical Images With Faster R-CNN

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2019.2946476

关键词

Medical services; Medical diagnostic imaging; Servers; Protocols; Cryptography; Object detection; Feature extraction; Privacy-preserving; faster R-CNN; medical images; additive secret sharing

资金

  1. National Natural Science Foundation of China [U1804263, U1764263, 61872283, 61702105]
  2. China 111 Project [B16037]
  3. College of Mathematics and Computer Science, Fuzhou University, China
  4. Fujian Provincial Key Laboratory of Information Security of Network Systems, Fuzhou, China

向作者/读者索取更多资源

This paper proposes a lightweight privacy-preserving Faster R-CNN framework (SecRCNN) for object detection in medical images. It improves the efficiency through the use of additive secret sharing technique and edge computing, and demonstrates the effectiveness and security through comprehensive theoretical analysis and extensive experiments.
In this paper, we propose a lightweight privacy-preserving Faster R-CNN framework (SecRCNN) for object detection in medical images. Faster R-CNN is one of the most outstanding deep learning models for object detection. Using SecRCNN, healthcare centers can efficiently complete privacy-preserving computations of Faster R-CNN via the additive secret sharing technique and edge computing. To implement SecRCNN, we design a series of interactive protocols to perform the three stages of Faster R-CNN, namely feature map extraction, region proposal and regression and classification. To improve the efficiency of SecRCNN, we improve the existing secure computation sub-protocols involved in SecRCNN, including division, exponentiation and logarithm. The newly proposed sub-protocols can dramatically reduce the number of messages exchanged during the iterative approximation process based on the coordinate rotation digital computer algorithm. Moreover, the effectiveness, efficiency and security of SecRCNN are demonstrated through comprehensive theoretical analysis and extensive experiments. The experimental findings show that the communication overhead in computing division, logarithm and exponentiation decreases to 36.19%, 73.82% and 43.37%, respectively.

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